Overview

Dataset statistics

Number of variables18
Number of observations16041
Missing cells2376
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory144.0 B

Variable types

Categorical9
Boolean1
Numeric8

Alerts

key_studentInfo has a high cardinality: 16041 distinct valuesHigh cardinality
Moyenne de score has a high cardinality: 1961 distinct valuesHigh cardinality
Nombre de date_submitted is highly overall correlated with Nombre de date and 2 other fieldsHigh correlation
Nombre de date is highly overall correlated with Nombre de date_submitted and 2 other fieldsHigh correlation
sum_click is highly overall correlated with Nombre de date_submitted and 2 other fieldsHigh correlation
Nombre de activity_type is highly overall correlated with Nombre de date_submitted and 2 other fieldsHigh correlation
disability is highly imbalanced (55.7%)Imbalance
imd_band has 599 (3.7%) missing valuesMissing
Moyenne pondérée de score has 1645 (10.3%) missing valuesMissing
key_studentInfo is uniformly distributedUniform
key_studentInfo has unique valuesUnique
num_of_prev_attempts has 14276 (89.0%) zerosZeros

Reproduction

Analysis started2023-03-21 10:24:03.234832
Analysis finished2023-03-21 10:24:11.254753
Duration8.02 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

key_studentInfo
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct16041
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size125.4 KiB
100064FFF2013J
 
1
607061DDD2013J
 
1
606908DDD2013J
 
1
606914BBB2014J
 
1
606918EEE2013J
 
1
Other values (16036)
16036 

Length

Max length15
Median length14
Mean length14.085905
Min length12

Characters and Unicode

Total characters225952
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16041 ?
Unique (%)100.0%

Sample

1st row100064FFF2013J
2nd row100282BBB2013J
3rd row100561DDD2014J
4th row100788CCC2014J
5th row100788FFF2013J

Common Values

ValueCountFrequency (%)
100064FFF2013J 1
 
< 0.1%
607061DDD2013J 1
 
< 0.1%
606908DDD2013J 1
 
< 0.1%
606914BBB2014J 1
 
< 0.1%
606918EEE2013J 1
 
< 0.1%
606930FFF2013J 1
 
< 0.1%
606939FFF2013J 1
 
< 0.1%
606966FFF2013J 1
 
< 0.1%
606990FFF2013J 1
 
< 0.1%
607011FFF2013J 1
 
< 0.1%
Other values (16031) 16031
99.9%

Length

2023-03-21T11:24:11.312559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100064fff2013j 1
 
< 0.1%
101781aaa2013j 1
 
< 0.1%
1045203aaa2014j 1
 
< 0.1%
104480ccc2014j 1
 
< 0.1%
104476aaa2013j 1
 
< 0.1%
1042726fff2014j 1
 
< 0.1%
100561ddd2014j 1
 
< 0.1%
100788ccc2014j 1
 
< 0.1%
100788fff2013j 1
 
< 0.1%
100893aaa2013j 1
 
< 0.1%
Other values (16031) 16031
99.9%

Most occurring characters

ValueCountFrequency (%)
2 25367
11.2%
1 24483
10.8%
0 24275
10.7%
4 18683
 
8.3%
J 16041
 
7.1%
3 15626
 
6.9%
6 13619
 
6.0%
5 13272
 
5.9%
F 11151
 
4.9%
B 10509
 
4.7%
Other values (8) 52926
23.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 161788
71.6%
Uppercase Letter 64164
 
28.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 25367
15.7%
1 24483
15.1%
0 24275
15.0%
4 18683
11.5%
3 15626
9.7%
6 13619
8.4%
5 13272
8.2%
8 9086
 
5.6%
9 8905
 
5.5%
7 8472
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
J 16041
25.0%
F 11151
17.4%
B 10509
16.4%
D 8799
13.7%
C 5994
 
9.3%
E 5286
 
8.2%
G 4269
 
6.7%
A 2115
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 161788
71.6%
Latin 64164
 
28.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 25367
15.7%
1 24483
15.1%
0 24275
15.0%
4 18683
11.5%
3 15626
9.7%
6 13619
8.4%
5 13272
8.2%
8 9086
 
5.6%
9 8905
 
5.5%
7 8472
 
5.2%
Latin
ValueCountFrequency (%)
J 16041
25.0%
F 11151
17.4%
B 10509
16.4%
D 8799
13.7%
C 5994
 
9.3%
E 5286
 
8.2%
G 4269
 
6.7%
A 2115
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 225952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 25367
11.2%
1 24483
10.8%
0 24275
10.7%
4 18683
 
8.3%
J 16041
 
7.1%
3 15626
 
6.9%
6 13619
 
6.0%
5 13272
 
5.9%
F 11151
 
4.9%
B 10509
 
4.7%
Other values (8) 52926
23.4%

final_result
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.4 KiB
Pass
8015 
Fail
3298 
Withdrawn
2815 
Distinction
1913 

Length

Max length11
Median length4
Mean length5.7122374
Min length4

Characters and Unicode

Total characters91630
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPass
2nd rowWithdrawn
3rd rowFail
4th rowDistinction
5th rowPass

Common Values

ValueCountFrequency (%)
Pass 8015
50.0%
Fail 3298
20.6%
Withdrawn 2815
 
17.5%
Distinction 1913
 
11.9%

Length

2023-03-21T11:24:11.404590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:24:11.512598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
pass 8015
50.0%
fail 3298
20.6%
withdrawn 2815
 
17.5%
distinction 1913
 
11.9%

Most occurring characters

ValueCountFrequency (%)
s 17943
19.6%
a 14128
15.4%
i 11852
12.9%
P 8015
8.7%
t 6641
 
7.2%
n 6641
 
7.2%
F 3298
 
3.6%
l 3298
 
3.6%
W 2815
 
3.1%
h 2815
 
3.1%
Other values (6) 14184
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75589
82.5%
Uppercase Letter 16041
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 17943
23.7%
a 14128
18.7%
i 11852
15.7%
t 6641
 
8.8%
n 6641
 
8.8%
l 3298
 
4.4%
h 2815
 
3.7%
d 2815
 
3.7%
r 2815
 
3.7%
w 2815
 
3.7%
Other values (2) 3826
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
P 8015
50.0%
F 3298
20.6%
W 2815
 
17.5%
D 1913
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 91630
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 17943
19.6%
a 14128
15.4%
i 11852
12.9%
P 8015
8.7%
t 6641
 
7.2%
n 6641
 
7.2%
F 3298
 
3.6%
l 3298
 
3.6%
W 2815
 
3.1%
h 2815
 
3.1%
Other values (6) 14184
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 17943
19.6%
a 14128
15.4%
i 11852
12.9%
P 8015
8.7%
t 6641
 
7.2%
n 6641
 
7.2%
F 3298
 
3.6%
l 3298
 
3.6%
W 2815
 
3.1%
h 2815
 
3.1%
Other values (6) 14184
15.5%

disability
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
False
14568 
True
1473 
ValueCountFrequency (%)
False 14568
90.8%
True 1473
 
9.2%
2023-03-21T11:24:11.608642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.4 KiB
269
5720 
268
4776 
262
3220 
261
2325 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48123
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row268
2nd row268
3rd row262
4th row269
5th row268

Common Values

ValueCountFrequency (%)
269 5720
35.7%
268 4776
29.8%
262 3220
20.1%
261 2325
14.5%

Length

2023-03-21T11:24:11.684642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:24:11.783612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
269 5720
35.7%
268 4776
29.8%
262 3220
20.1%
261 2325
14.5%

Most occurring characters

ValueCountFrequency (%)
2 19261
40.0%
6 16041
33.3%
9 5720
 
11.9%
8 4776
 
9.9%
1 2325
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48123
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19261
40.0%
6 16041
33.3%
9 5720
 
11.9%
8 4776
 
9.9%
1 2325
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 48123
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 19261
40.0%
6 16041
33.3%
9 5720
 
11.9%
8 4776
 
9.9%
1 2325
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 19261
40.0%
6 16041
33.3%
9 5720
 
11.9%
8 4776
 
9.9%
1 2325
 
4.8%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.4 KiB
M
8909 
F
7132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16041
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 8909
55.5%
F 7132
44.5%

Length

2023-03-21T11:24:11.872984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:24:11.960985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
m 8909
55.5%
f 7132
44.5%

Most occurring characters

ValueCountFrequency (%)
M 8909
55.5%
F 7132
44.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16041
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 8909
55.5%
F 7132
44.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 16041
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 8909
55.5%
F 7132
44.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16041
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 8909
55.5%
F 7132
44.5%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.4 KiB
A Level or Equivalent
7148 
Lower Than A Level
6070 
HE Qualification
2536 
Post Graduate Qualification
 
162
No Formal quals
 
125

Length

Max length27
Median length21
Mean length19.088149
Min length15

Characters and Unicode

Total characters306193
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA Level or Equivalent
2nd rowLower Than A Level
3rd rowLower Than A Level
4th rowHE Qualification
5th rowHE Qualification

Common Values

ValueCountFrequency (%)
A Level or Equivalent 7148
44.6%
Lower Than A Level 6070
37.8%
HE Qualification 2536
 
15.8%
Post Graduate Qualification 162
 
1.0%
No Formal quals 125
 
0.8%

Length

2023-03-21T11:24:12.047052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:24:12.156224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
a 13218
22.5%
level 13218
22.5%
or 7148
12.2%
equivalent 7148
12.2%
lower 6070
10.3%
than 6070
10.3%
qualification 2698
 
4.6%
he 2536
 
4.3%
post 162
 
0.3%
graduate 162
 
0.3%
Other values (3) 375
 
0.6%

Most occurring characters

ValueCountFrequency (%)
42764
14.0%
e 39816
13.0%
l 23314
 
7.6%
v 20366
 
6.7%
L 19288
 
6.3%
a 19188
 
6.3%
o 16328
 
5.3%
n 15916
 
5.2%
i 15242
 
5.0%
r 13505
 
4.4%
Other values (19) 80466
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 209361
68.4%
Uppercase Letter 54068
 
17.7%
Space Separator 42764
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 39816
19.0%
l 23314
11.1%
v 20366
9.7%
a 19188
9.2%
o 16328
7.8%
n 15916
 
7.6%
i 15242
 
7.3%
r 13505
 
6.5%
t 10170
 
4.9%
u 10133
 
4.8%
Other values (8) 25383
12.1%
Uppercase Letter
ValueCountFrequency (%)
L 19288
35.7%
A 13218
24.4%
E 9684
17.9%
T 6070
 
11.2%
Q 2698
 
5.0%
H 2536
 
4.7%
P 162
 
0.3%
G 162
 
0.3%
N 125
 
0.2%
F 125
 
0.2%
Space Separator
ValueCountFrequency (%)
42764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 263429
86.0%
Common 42764
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 39816
15.1%
l 23314
 
8.9%
v 20366
 
7.7%
L 19288
 
7.3%
a 19188
 
7.3%
o 16328
 
6.2%
n 15916
 
6.0%
i 15242
 
5.8%
r 13505
 
5.1%
A 13218
 
5.0%
Other values (18) 67248
25.5%
Common
ValueCountFrequency (%)
42764
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 306193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42764
14.0%
e 39816
13.0%
l 23314
 
7.6%
v 20366
 
6.7%
L 19288
 
6.3%
a 19188
 
6.3%
o 16328
 
5.3%
n 15916
 
5.2%
i 15242
 
5.0%
r 13505
 
4.4%
Other values (19) 80466
26.3%

imd_band
Categorical

Distinct10
Distinct (%)0.1%
Missing599
Missing (%)3.7%
Memory size125.4 KiB
30-40%
1725 
20-30%
1662 
10-20
1621 
50-60%
1606 
40-50%
1605 
Other values (5)
7223 

Length

Max length7
Median length6
Mean length5.8879679
Min length5

Characters and Unicode

Total characters90922
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0-10%
2nd row20-30%
3rd row70-80%
4th row80-90%
5th row80-90%

Common Values

ValueCountFrequency (%)
30-40% 1725
10.8%
20-30% 1662
10.4%
10-20 1621
10.1%
50-60% 1606
10.0%
40-50% 1605
10.0%
60-70% 1503
9.4%
70-80% 1500
9.4%
0-10% 1452
9.1%
80-90% 1425
8.9%
90-100% 1343
8.4%
(Missing) 599
 
3.7%

Length

2023-03-21T11:24:12.274258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:24:12.410256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
30-40 1725
11.2%
20-30 1662
10.8%
10-20 1621
10.5%
50-60 1606
10.4%
40-50 1605
10.4%
60-70 1503
9.7%
70-80 1500
9.7%
0-10 1452
9.4%
80-90 1425
9.2%
90-100 1343
8.7%

Most occurring characters

ValueCountFrequency (%)
0 32227
35.4%
- 15442
17.0%
% 13821
15.2%
1 4416
 
4.9%
3 3387
 
3.7%
4 3330
 
3.7%
2 3283
 
3.6%
5 3211
 
3.5%
6 3109
 
3.4%
7 3003
 
3.3%
Other values (2) 5693
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61659
67.8%
Dash Punctuation 15442
 
17.0%
Other Punctuation 13821
 
15.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32227
52.3%
1 4416
 
7.2%
3 3387
 
5.5%
4 3330
 
5.4%
2 3283
 
5.3%
5 3211
 
5.2%
6 3109
 
5.0%
7 3003
 
4.9%
8 2925
 
4.7%
9 2768
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 15442
100.0%
Other Punctuation
ValueCountFrequency (%)
% 13821
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32227
35.4%
- 15442
17.0%
% 13821
15.2%
1 4416
 
4.9%
3 3387
 
3.7%
4 3330
 
3.7%
2 3283
 
3.6%
5 3211
 
3.5%
6 3109
 
3.4%
7 3003
 
3.3%
Other values (2) 5693
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32227
35.4%
- 15442
17.0%
% 13821
15.2%
1 4416
 
4.9%
3 3387
 
3.7%
4 3330
 
3.7%
2 3283
 
3.6%
5 3211
 
3.5%
6 3109
 
3.4%
7 3003
 
3.3%
Other values (2) 5693
 
6.3%

num_of_prev_attempts
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13864472
Minimum0
Maximum6
Zeros14276
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size125.4 KiB
2023-03-21T11:24:12.526274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44072982
Coefficient of variation (CV)3.1788431
Kurtosis22.072453
Mean0.13864472
Median Absolute Deviation (MAD)0
Skewness4.0796659
Sum2224
Variance0.19424278
MonotonicityNot monotonic
2023-03-21T11:24:12.598287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 14276
89.0%
1 1411
 
8.8%
2 277
 
1.7%
3 56
 
0.3%
4 15
 
0.1%
5 5
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 14276
89.0%
1 1411
 
8.8%
2 277
 
1.7%
3 56
 
0.3%
4 15
 
0.1%
5 5
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 5
 
< 0.1%
4 15
 
0.1%
3 56
 
0.3%
2 277
 
1.7%
1 1411
 
8.8%
0 14276
89.0%

region
Categorical

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size125.4 KiB
Scotland
1909 
East Anglian Region
1623 
South Region
1536 
London Region
1428 
North Western Region
1338 
Other values (8)
8207 

Length

Max length20
Median length17
Mean length14.393118
Min length5

Characters and Unicode

Total characters230880
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest Midlands Region
2nd rowWales
3rd rowEast Anglian Region
4th rowScotland
5th rowScotland

Common Values

ValueCountFrequency (%)
Scotland 1909
11.9%
East Anglian Region 1623
10.1%
South Region 1536
9.6%
London Region 1428
8.9%
North Western Region 1338
8.3%
West Midlands Region 1193
7.4%
South West Region 1187
7.4%
East Midlands Region 1135
7.1%
Wales 1087
6.8%
South East Region 1063
6.6%
Other values (3) 2542
15.8%

Length

2023-03-21T11:24:12.698256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
region 12339
34.4%
east 3821
 
10.6%
south 3786
 
10.5%
west 2380
 
6.6%
midlands 2328
 
6.5%
north 2225
 
6.2%
scotland 1909
 
5.3%
anglian 1623
 
4.5%
london 1428
 
4.0%
western 1338
 
3.7%
Other values (3) 2742
 
7.6%

Most occurring characters

ValueCountFrequency (%)
n 24722
10.7%
o 24064
10.4%
e 20137
 
8.7%
19878
 
8.6%
i 17239
 
7.5%
t 15459
 
6.7%
g 13962
 
6.0%
R 12339
 
5.3%
s 11903
 
5.2%
a 11474
 
5.0%
Other values (16) 59703
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 175083
75.8%
Uppercase Letter 35919
 
15.6%
Space Separator 19878
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 24722
14.1%
o 24064
13.7%
e 20137
11.5%
i 17239
9.8%
t 15459
8.8%
g 13962
8.0%
s 11903
6.8%
a 11474
6.6%
d 8699
 
5.0%
l 7653
 
4.4%
Other values (5) 19771
11.3%
Uppercase Letter
ValueCountFrequency (%)
R 12339
34.4%
S 5695
15.9%
W 4805
 
13.4%
E 3821
 
10.6%
M 2328
 
6.5%
N 2225
 
6.2%
A 1623
 
4.5%
L 1428
 
4.0%
Y 949
 
2.6%
I 706
 
2.0%
Space Separator
ValueCountFrequency (%)
19878
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 211002
91.4%
Common 19878
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 24722
11.7%
o 24064
11.4%
e 20137
 
9.5%
i 17239
 
8.2%
t 15459
 
7.3%
g 13962
 
6.6%
R 12339
 
5.8%
s 11903
 
5.6%
a 11474
 
5.4%
d 8699
 
4.1%
Other values (15) 51004
24.2%
Common
ValueCountFrequency (%)
19878
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 24722
10.7%
o 24064
10.4%
e 20137
 
8.7%
19878
 
8.6%
i 17239
 
7.5%
t 15459
 
6.7%
g 13962
 
6.0%
R 12339
 
5.3%
s 11903
 
5.2%
a 11474
 
5.0%
Other values (16) 59703
25.9%

studied_credits
Real number (ℝ)

Distinct43
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.869958
Minimum30
Maximum430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size125.4 KiB
2023-03-21T11:24:12.822267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q160
median60
Q390
95-th percentile135
Maximum430
Range400
Interquartile range (IQR)30

Descriptive statistics

Standard deviation37.288836
Coefficient of variation (CV)0.49148354
Kurtosis4.6318209
Mean75.869958
Median Absolute Deviation (MAD)0
Skewness1.606108
Sum1217030
Variance1390.4573
MonotonicityNot monotonic
2023-03-21T11:24:12.927622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
60 8476
52.8%
120 2862
 
17.8%
30 2126
 
13.3%
90 1595
 
9.9%
150 327
 
2.0%
180 283
 
1.8%
240 68
 
0.4%
210 53
 
0.3%
70 38
 
0.2%
75 38
 
0.2%
Other values (33) 175
 
1.1%
ValueCountFrequency (%)
30 2126
 
13.3%
40 11
 
0.1%
45 23
 
0.1%
50 5
 
< 0.1%
55 2
 
< 0.1%
60 8476
52.8%
65 1
 
< 0.1%
70 38
 
0.2%
75 38
 
0.2%
80 10
 
0.1%
ValueCountFrequency (%)
430 1
 
< 0.1%
420 1
 
< 0.1%
360 1
 
< 0.1%
345 1
 
< 0.1%
330 4
 
< 0.1%
300 10
 
0.1%
270 11
 
0.1%
250 1
 
< 0.1%
240 68
0.4%
220 6
 
< 0.1%

age_band
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.4 KiB
0-35
11079 
35-55
4822 
55<=
 
140

Length

Max length5
Median length4
Mean length4.3006047
Min length4

Characters and Unicode

Total characters68986
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row35-55
2nd row0-35
3rd row0-35
4th row0-35
5th row0-35

Common Values

ValueCountFrequency (%)
0-35 11079
69.1%
35-55 4822
30.1%
55<= 140
 
0.9%

Length

2023-03-21T11:24:13.033615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-21T11:24:13.130503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0-35 11079
69.1%
35-55 4822
30.1%
55 140
 
0.9%

Most occurring characters

ValueCountFrequency (%)
5 25825
37.4%
- 15901
23.0%
3 15901
23.0%
0 11079
16.1%
< 140
 
0.2%
= 140
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52805
76.5%
Dash Punctuation 15901
 
23.0%
Math Symbol 280
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 25825
48.9%
3 15901
30.1%
0 11079
21.0%
Math Symbol
ValueCountFrequency (%)
< 140
50.0%
= 140
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 15901
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68986
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 25825
37.4%
- 15901
23.0%
3 15901
23.0%
0 11079
16.1%
< 140
 
0.2%
= 140
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68986
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 25825
37.4%
- 15901
23.0%
3 15901
23.0%
0 11079
16.1%
< 140
 
0.2%
= 140
 
0.2%

Nombre de date_submitted
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1159529
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size125.4 KiB
2023-03-21T11:24:13.209549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4962068
Coefficient of variation (CV)0.57165365
Kurtosis-1.004013
Mean6.1159529
Median Absolute Deviation (MAD)3
Skewness0.37257927
Sum98106
Variance12.223462
MonotonicityNot monotonic
2023-03-21T11:24:13.290251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 2487
15.5%
4 2165
13.5%
12 2122
13.2%
6 1872
11.7%
1 1520
9.5%
2 1158
7.2%
3 1071
6.7%
8 1027
6.4%
11 1011
6.3%
9 891
 
5.6%
Other values (2) 717
 
4.5%
ValueCountFrequency (%)
1 1520
9.5%
2 1158
7.2%
3 1071
6.7%
4 2165
13.5%
5 2487
15.5%
6 1872
11.7%
7 420
 
2.6%
8 1027
6.4%
9 891
 
5.6%
10 297
 
1.9%
ValueCountFrequency (%)
12 2122
13.2%
11 1011
6.3%
10 297
 
1.9%
9 891
 
5.6%
8 1027
6.4%
7 420
 
2.6%
6 1872
11.7%
5 2487
15.5%
4 2165
13.5%
3 1071
6.7%

Moyenne de score
Categorical

Distinct1961
Distinct (%)12.2%
Missing12
Missing (%)0.1%
Memory size125.4 KiB
78,00
 
140
75,00
 
136
80,00
 
136
70,00
 
135
100,00
 
130
Other values (1956)
15352 

Length

Max length6
Median length5
Mean length5.0007486
Min length4

Characters and Unicode

Total characters80157
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique490 ?
Unique (%)3.1%

Sample

1st row89,33
2nd row78,00
3rd row73,17
4th row90,00
5th row88,58

Common Values

ValueCountFrequency (%)
78,00 140
 
0.9%
75,00 136
 
0.8%
80,00 136
 
0.8%
70,00 135
 
0.8%
100,00 130
 
0.8%
76,00 126
 
0.8%
85,00 121
 
0.8%
67,00 115
 
0.7%
65,00 113
 
0.7%
72,00 102
 
0.6%
Other values (1951) 14775
92.1%

Length

2023-03-21T11:24:13.380457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78,00 140
 
0.9%
80,00 136
 
0.8%
75,00 136
 
0.8%
70,00 135
 
0.8%
100,00 130
 
0.8%
76,00 126
 
0.8%
85,00 121
 
0.8%
67,00 115
 
0.7%
65,00 113
 
0.7%
72,00 102
 
0.6%
Other values (1951) 14775
92.2%

Most occurring characters

ValueCountFrequency (%)
, 16029
20.0%
0 15157
18.9%
8 8424
10.5%
7 8377
10.5%
5 7089
8.8%
6 6290
 
7.8%
3 4875
 
6.1%
2 3845
 
4.8%
9 3806
 
4.7%
4 3456
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 64128
80.0%
Other Punctuation 16029
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15157
23.6%
8 8424
13.1%
7 8377
13.1%
5 7089
11.1%
6 6290
9.8%
3 4875
 
7.6%
2 3845
 
6.0%
9 3806
 
5.9%
4 3456
 
5.4%
1 2809
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 16029
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 16029
20.0%
0 15157
18.9%
8 8424
10.5%
7 8377
10.5%
5 7089
8.8%
6 6290
 
7.8%
3 4875
 
6.1%
2 3845
 
4.8%
9 3806
 
4.7%
4 3456
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 16029
20.0%
0 15157
18.9%
8 8424
10.5%
7 8377
10.5%
5 7089
8.8%
6 6290
 
7.8%
3 4875
 
6.1%
2 3845
 
4.8%
9 3806
 
4.7%
4 3456
 
4.3%
Distinct7994
Distinct (%)55.5%
Missing1645
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean70.549548
Minimum0
Maximum100
Zeros22
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size125.4 KiB
2023-03-21T11:24:13.499164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q160.569409
median72.946216
Q383.212423
95-th percentile93
Maximum100
Range100
Interquartile range (IQR)22.643014

Descriptive statistics

Standard deviation16.669768
Coefficient of variation (CV)0.23628454
Kurtosis0.78794921
Mean70.549548
Median Absolute Deviation (MAD)11.053784
Skewness-0.83623814
Sum1015631.3
Variance277.88115
MonotonicityNot monotonic
2023-03-21T11:24:13.783089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 92
 
0.6%
78 90
 
0.6%
56 87
 
0.5%
67 81
 
0.5%
60 71
 
0.4%
66 68
 
0.4%
72 67
 
0.4%
68 62
 
0.4%
76 60
 
0.4%
65 58
 
0.4%
Other values (7984) 13660
85.2%
(Missing) 1645
 
10.3%
ValueCountFrequency (%)
0 22
0.1%
0.696929239 1
 
< 0.1%
1.636363636 1
 
< 0.1%
1.750170184 1
 
< 0.1%
2.168224299 1
 
< 0.1%
2.284379172 1
 
< 0.1%
3.407209613 1
 
< 0.1%
4 2
 
< 0.1%
6 3
 
< 0.1%
6.47129506 1
 
< 0.1%
ValueCountFrequency (%)
100 26
0.2%
99.72 1
 
< 0.1%
99.68 1
 
< 0.1%
99.56 2
 
< 0.1%
99.52 1
 
< 0.1%
99.44 1
 
< 0.1%
99.38 1
 
< 0.1%
99.24 1
 
< 0.1%
99.22 1
 
< 0.1%
99.16 1
 
< 0.1%

date_registration
Real number (ℝ)

Distinct220
Distinct (%)1.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-68.638608
Minimum-205
Maximum101
Zeros2
Zeros (%)< 0.1%
Negative15902
Negative (%)99.1%
Memory size125.4 KiB
2023-03-21T11:24:13.900512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-205
5-th percentile-155
Q1-101
median-57
Q3-31
95-th percentile-14
Maximum101
Range306
Interquartile range (IQR)70

Descriptive statistics

Standard deviation46.251046
Coefficient of variation (CV)-0.67383427
Kurtosis-0.44587467
Mean-68.638608
Median Absolute Deviation (MAD)30
Skewness-0.70114397
Sum-1100826
Variance2139.1593
MonotonicityNot monotonic
2023-03-21T11:24:14.013949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-29 452
 
2.8%
-22 365
 
2.3%
-32 358
 
2.2%
-38 342
 
2.1%
-30 328
 
2.0%
-23 317
 
2.0%
-31 291
 
1.8%
-36 276
 
1.7%
-25 275
 
1.7%
-53 265
 
1.7%
Other values (210) 12769
79.6%
ValueCountFrequency (%)
-205 5
 
< 0.1%
-204 1
 
< 0.1%
-203 9
0.1%
-202 12
0.1%
-201 2
 
< 0.1%
-200 5
 
< 0.1%
-199 7
< 0.1%
-198 14
0.1%
-197 4
 
< 0.1%
-196 15
0.1%
ValueCountFrequency (%)
101 1
 
< 0.1%
48 1
 
< 0.1%
44 1
 
< 0.1%
32 1
 
< 0.1%
27 1
 
< 0.1%
24 1
 
< 0.1%
23 3
< 0.1%
20 1
 
< 0.1%
19 3
< 0.1%
17 6
< 0.1%

Nombre de date
Real number (ℝ)

Distinct1667
Distinct (%)10.4%
Missing39
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean408.72285
Minimum1
Maximum2953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size125.4 KiB
2023-03-21T11:24:14.130221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile39.05
Q1140
median291
Q3558
95-th percentile1184.95
Maximum2953
Range2952
Interquartile range (IQR)418

Descriptive statistics

Standard deviation380.95703
Coefficient of variation (CV)0.93206689
Kurtosis4.5273652
Mean408.72285
Median Absolute Deviation (MAD)181
Skewness1.8483527
Sum6540383
Variance145128.26
MonotonicityNot monotonic
2023-03-21T11:24:14.237243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 46
 
0.3%
93 45
 
0.3%
102 44
 
0.3%
106 43
 
0.3%
69 43
 
0.3%
128 42
 
0.3%
67 42
 
0.3%
110 41
 
0.3%
180 41
 
0.3%
150 41
 
0.3%
Other values (1657) 15574
97.1%
ValueCountFrequency (%)
1 10
 
0.1%
2 14
0.1%
3 7
 
< 0.1%
4 30
0.2%
5 13
0.1%
6 8
 
< 0.1%
7 15
0.1%
8 12
 
0.1%
9 20
0.1%
10 13
0.1%
ValueCountFrequency (%)
2953 1
< 0.1%
2945 2
< 0.1%
2917 1
< 0.1%
2878 1
< 0.1%
2819 1
< 0.1%
2806 1
< 0.1%
2775 1
< 0.1%
2753 1
< 0.1%
2747 1
< 0.1%
2732 1
< 0.1%

sum_click
Real number (ℝ)

Distinct4520
Distinct (%)28.2%
Missing39
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1533.5364
Minimum1
Maximum24139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size125.4 KiB
2023-03-21T11:24:14.349485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile98
Q1405
median928
Q31992.75
95-th percentile4992.85
Maximum24139
Range24138
Interquartile range (IQR)1587.75

Descriptive statistics

Standard deviation1789.2244
Coefficient of variation (CV)1.1667309
Kurtosis14.952522
Mean1533.5364
Median Absolute Deviation (MAD)638
Skewness2.9776229
Sum24539650
Variance3201323.8
MonotonicityNot monotonic
2023-03-21T11:24:14.465935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118 21
 
0.1%
395 20
 
0.1%
243 19
 
0.1%
277 19
 
0.1%
381 19
 
0.1%
106 19
 
0.1%
269 18
 
0.1%
332 18
 
0.1%
326 17
 
0.1%
334 17
 
0.1%
Other values (4510) 15815
98.6%
(Missing) 39
 
0.2%
ValueCountFrequency (%)
1 6
 
< 0.1%
2 6
 
< 0.1%
3 11
0.1%
4 15
0.1%
5 8
< 0.1%
6 6
 
< 0.1%
7 6
 
< 0.1%
8 10
0.1%
9 6
 
< 0.1%
10 6
 
< 0.1%
ValueCountFrequency (%)
24139 1
< 0.1%
20391 1
< 0.1%
19734 1
< 0.1%
19461 1
< 0.1%
19415 1
< 0.1%
19126 1
< 0.1%
17825 1
< 0.1%
17481 1
< 0.1%
17246 1
< 0.1%
16440 1
< 0.1%

Nombre de activity_type
Real number (ℝ)

Distinct306
Distinct (%)1.9%
Missing39
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean72.927509
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size125.4 KiB
2023-03-21T11:24:14.579587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q136
median58
Q397
95-th percentile181
Maximum360
Range359
Interquartile range (IQR)61

Descriptive statistics

Standard deviation52.088688
Coefficient of variation (CV)0.71425294
Kurtosis2.0342114
Mean72.927509
Median Absolute Deviation (MAD)27
Skewness1.380553
Sum1166986
Variance2713.2314
MonotonicityNot monotonic
2023-03-21T11:24:14.688261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 214
 
1.3%
47 212
 
1.3%
48 201
 
1.3%
44 195
 
1.2%
51 193
 
1.2%
39 190
 
1.2%
43 190
 
1.2%
56 188
 
1.2%
49 185
 
1.2%
38 185
 
1.2%
Other values (296) 14049
87.6%
ValueCountFrequency (%)
1 28
 
0.2%
2 23
 
0.1%
3 35
0.2%
4 38
0.2%
5 28
 
0.2%
6 51
0.3%
7 58
0.4%
8 52
0.3%
9 52
0.3%
10 70
0.4%
ValueCountFrequency (%)
360 1
< 0.1%
340 2
< 0.1%
325 2
< 0.1%
324 1
< 0.1%
323 1
< 0.1%
321 2
< 0.1%
316 1
< 0.1%
314 1
< 0.1%
310 1
< 0.1%
308 1
< 0.1%

Interactions

2023-03-21T11:24:09.815340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:04.326609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:05.087964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:05.877305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:06.622219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:07.524645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.289288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:09.061331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:09.911342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:04.418636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:05.189694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:05.973337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:06.718189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:07.620251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.385288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:09.153330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:10.023339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:04.526642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:05.289692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:06.069304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:06.826220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:07.722647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.489316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:09.257332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:10.116194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:04.618638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:05.393406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:06.161333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:06.918218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:07.814657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.581287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:09.347439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:10.212240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:04.710643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:05.485743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:06.249332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:07.002675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:07.907568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.677328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:09.438605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:10.312221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:04.804285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:05.585286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:06.342618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:07.098675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.002123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.773326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:09.534605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:10.408191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:04.900284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:05.680604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:06.437201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:07.338998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.098096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.869302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:09.626608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:10.504224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:04.992255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:05.777308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:06.527429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:07.428646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.190123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:08.961306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-21T11:24:09.715313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-21T11:24:14.800258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
num_of_prev_attemptsstudied_creditsNombre de date_submittedMoyenne pondérée de scoredate_registrationNombre de datesum_clickNombre de activity_typefinal_resultdisabilitymodule_presentation_lengthgenderhighest_educationimd_bandregionage_band
num_of_prev_attempts1.0000.045-0.065-0.115-0.035-0.131-0.137-0.1030.0880.0560.0360.0230.0160.0160.0180.003
studied_credits0.0451.000-0.023-0.060-0.0580.0840.0540.1520.0840.0510.0600.0940.0340.0110.0240.080
Nombre de date_submitted-0.065-0.0231.0000.171-0.0180.5360.5170.5320.4270.0910.4500.3100.0850.0370.0500.069
Moyenne pondérée de score-0.115-0.0600.1711.000-0.0030.3990.3970.2820.3640.0680.1450.1270.0880.0440.0280.055
date_registration-0.035-0.058-0.018-0.0031.000-0.111-0.086-0.1150.0220.0000.1410.0280.0360.0140.0840.022
Nombre de date-0.1310.0840.5360.399-0.1111.0000.9600.8860.2890.0410.0900.1600.0610.0290.0240.128
sum_click-0.1370.0540.5170.397-0.0860.9601.0000.8370.1960.0410.1360.1410.0350.0170.0110.111
Nombre de activity_type-0.1030.1520.5320.282-0.1150.8860.8371.0000.2770.0340.1230.1660.0590.0290.0290.096
final_result0.0880.0840.4270.3640.0220.2890.1960.2771.0000.0750.0670.0270.0920.0740.0480.057
disability0.0560.0510.0910.0680.0000.0410.0410.0340.0751.0000.0390.0430.1030.0580.0880.036
module_presentation_length0.0360.0600.4500.1450.1410.0900.1360.1230.0670.0391.0000.2920.0420.0210.0750.016
gender0.0230.0940.3100.1270.0280.1600.1410.1660.0270.0430.2921.0000.0710.0800.0810.071
highest_education0.0160.0340.0850.0880.0360.0610.0350.0590.0920.1030.0420.0711.0000.0760.1400.138
imd_band0.0160.0110.0370.0440.0140.0290.0170.0290.0740.0580.0210.0800.0761.0000.1240.071
region0.0180.0240.0500.0280.0840.0240.0110.0290.0480.0880.0750.0810.1400.1241.0000.060
age_band0.0030.0800.0690.0550.0220.1280.1110.0960.0570.0360.0160.0710.1380.0710.0601.000

Missing values

2023-03-21T11:24:10.672219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-21T11:24:10.941244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-21T11:24:11.151006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

key_studentInfofinal_resultdisabilitymodule_presentation_lengthgenderhighest_educationimd_bandnum_of_prev_attemptsregionstudied_creditsage_bandNombre de date_submittedMoyenne de scoreMoyenne pondérée de scoredate_registrationNombre de datesum_clickNombre de activity_type
0100064FFF2013JPassN268FA Level or Equivalent0-10%0West Midlands Region6035-551289,3392.000000-136.01632.06514.0242.0
1100282BBB2013JWithdrawnN268FLower Than A Level20-30%1Wales1200-35178,0078.000000-54.016.042.09.0
2100561DDD2014JFailN262MLower Than A Level70-80%1East Anglian Region600-35673,1772.500000-35.0466.0950.095.0
3100788CCC2014JDistinctionN269MHE Qualification80-90%1Scotland600-35490,0087.720000-103.0440.01261.078.0
4100788FFF2013JPassN268MHE Qualification80-90%0Scotland900-351288,5879.158977-82.0820.03134.0168.0
5100893AAA2013JPassN268MA Level or Equivalent20-30%0Yorkshire Region600-35568,4068.700000-62.0243.0744.047.0
6101116AAA2014JPassN269FLower Than A Level60-70%0East Anglian Region6035-55582,6082.900000-80.0662.01769.064.0
7101217FFF2013JWithdrawnY268MA Level or Equivalent40-50%1London Region9035-55114,0014.000000-121.0156.0332.064.0
8101217FFF2014JFailY269MA Level or Equivalent40-50%2London Region6035-55168,0068.000000-44.0239.0385.057.0
9101279DDD2014JFailN262FA Level or Equivalent0-10%0Yorkshire Region6035-55155,0055.000000-144.052.0112.027.0
key_studentInfofinal_resultdisabilitymodule_presentation_lengthgenderhighest_educationimd_bandnum_of_prev_attemptsregionstudied_creditsage_bandNombre de date_submittedMoyenne de scoreMoyenne pondérée de scoredate_registrationNombre de datesum_clickNombre de activity_type
16031985062FFF2013JPassN268MHE Qualification50-60%0South West Region12035-551284,0071.697579-81.0759.03615.0239.0
16032986561CCC2014JPassN269MHE Qualification90-100%0South East Region6055<=890,1389.400000-148.0439.01462.080.0
1603398720BBB2013JPassY268FLower Than A Level20-30%0London Region6035-551172,2774.250000-186.0157.0439.030.0
16034988019FFF2013JFailY268FA Level or Equivalent20-30%1London Region6035-55969,7836.635126-128.0591.02044.0126.0
1603598842BBB2014JPassN262FLower Than A Level10-200South Region600-35559,4048.250000-25.093.0368.033.0
16036990155FFF2013JDistinctionN268MHE Qualification80-90%0South Region9035-551289,1792.485610-38.0792.03380.0183.0
1603799075DDD2014JFailN262MA Level or Equivalent30-40%0South Region600-35554,2052.733333-59.0692.01369.0113.0
1603899088BBB2014JWithdrawnN262FLower Than A Level20-30%2Yorkshire Region600-35329,6744.333333-31.0110.0359.045.0
16039991843EEE2013JDistinctionN268FA Level or Equivalent80-90%0Scotland3035-55495,5095.320000-93.0991.05137.067.0
16040998493AAA2014JPassN269FA Level or Equivalent90-100%0South Region6055<=568,8067.600000-135.02065.010464.097.0